Reputation-based decisions for logic-based cognitive agents

• Published in: Autonomous agents and multi-agent systems Vol. 24; no. 1; pp. 175 - 216
• Main Authors: Pinyol, Isaac, Sabater-Mir, Jordi, Dellunde, Pilar, Paolucci, Mario
• Format: Journal Article
• Language: English
• Published: Boston Springer US 2012
• Subjects: Artificial Intelligence; Computer Science; Computer Systems Organization and Communication Networks; Software Engineering/Programming and Operating Systems; User Interfaces and Human Computer Interaction; Belief logic; Multi-context systems; Reputation; Image; BDI agents; Trust
• ISSN: 1387-2532; 1573-7454
• DOI: 10.1007/s10458-010-9149-y

Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. First, we specify a belief logic capable to capture the semantics of Repage information, which encodes probabilities. This logic is defined by means of a two first-order languages hierarchy, allowing the specification of axioms as first-order theories. The belief logic integrates the information coming from Repage in terms if image and reputation, and combines them, defining a typology of agents depending of such combination. We use this logic to build a complete graded BDI model specified as a multi-context system where beliefs, desires, intentions and plans interact among each other to perform a BDI reasoning. We conclude the paper with an example and a related work section that compares our approach with current state-of-the-art models.